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Generative music with stochastic diffusion search

Generative music with stochastic diffusion search

Al-Rifaie, Asmaa Majid and Al-Rifaie, Mohammad Majid ORCID: 0000-0002-1798-9615 (2015) Generative music with stochastic diffusion search. In: EvoMUSART 2015: Evolutionary and Biologically Inspired Music, Sound, Art and Design. Lecture Notes in Computer Science book series (LNCS), 9027 . Springer, Cham, Switzerland, pp. 1-14. ISBN 978-3319164977 ISSN 0302-9743 (Print), 1611-3349 (Online) (doi:https://doi.org/10.1007/978-3-319-16498-4_1)

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Abstract

This paper introduces an approach for using a swarm intelligence algorithm, Stochastic Diffusion Search (SDS) – inspired by one
species of ants, Leptothorax acervorum – in order to generate music from plain text. In this approach , SDS is adapted in such a way to vocalise the agents, to hear their “chit-chat” . While the generated music depends on the input text, the algorithm’s search capability in locating the words in the input text is reflected in the duration and dynamic of the resulting musical notes. In other words, the generated music depends on the behaviour of the algorithm and the communication between its agents. This novel approach, while staying loyal to the original input text, when run each time, ‘vocalises’ the input text in varying ‘flavours’.

Item Type: Conference Proceedings
Title of Proceedings: EvoMUSART 2015: Evolutionary and Biologically Inspired Music, Sound, Art and Design
Uncontrolled Keywords: Stochastic diffusion search, music, swarm intelligence, stochastic diffusion search, generative music, nature-inspired algorithm.
Subjects: M Music and Books on Music > M Music
Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Centre for Computer & Computational Science
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 21 Jul 2021 10:04
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/21009

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